×

Learning method and testing method for monitoring blind spot of vehicle, and learning device and testing device using the same

  • US 10,474,930 B1
  • Filed: 10/05/2018
  • Issued: 11/12/2019
  • Est. Priority Date: 10/05/2018
  • Status: Active Grant
First Claim
Patent Images

1. A learning method of a CNN (Convolutional Neural Network) for monitoring one or more blind spots of a monitoring vehicle, comprising steps of:

  • (a) a learning device, if a training image corresponding to at least one video image taken from the monitoring vehicle is inputted, instructing a detector on the monitoring vehicle to output class information and location information on a monitored vehicle included in the training image;

    (b) the learning device instructing a cue information extracting layer to perform one or more operations by using the class information and the location information on the monitored vehicle, to thereby output one or more pieces of cue information on the monitored vehicle, and instructing an FC layer (fully connected layer) for monitoring the blind spots to perform one or more neural network operations by using said pieces of cue information or their processed values on the monitored vehicle, to thereby output a result of determining whether the monitored vehicle is located on one of the blind spots of the monitoring vehicle; and

    (c) the learning device instructing a first loss layer to generate one or more loss values for the blind spots by referring to the result and its corresponding first GT (Ground Truth), to thereby learn one or more parameters of the FC layer for monitoring the blind spots by backpropagating the loss values for the blind spots, and instructing a second loss layer to generate one or more loss values for vehicle detection by referring to the class information and the location information on the monitored vehicle and their corresponding second GT, to thereby learn one or more parameters of the detector by backpropagating the loss values for the vehicle detection;

    wherein the detector is a vehicle detector based on an R-CNN (Region-based Convolutional Neural Network) including;

    one or more convolutional layers which generate a feature map from the training image,an RPN (Region Proposal Network) which generates an ROI (Region Of Interest) of the monitored vehicle from the feature map,a pooling layer which generates a feature vector by pooling an area, in the feature map, corresponding to the ROI,at least one FC layer for the vehicle detection which performs at least one fully connected operation on the feature vector, to thereby generate one or more FC output values,a classification layer which outputs the class information on the monitored vehicle by referring to the FC output values, anda regression layer which outputs the location information on the monitored vehicle by referring to the FC output values.

View all claims
  • 1 Assignment
Timeline View
Assignment View
    ×
    ×